Causal Differential Skill Circuits for Localizing and Repairing Bias in Tool-Using Agents

Fairness evaluations of tool-using large language model agents can detect counterfactual disparities in final actions and tool use, but cannot localize the portions of an execution workflow that transmit these disparities. Localization is challenging because agent workflows contain ordered interactions among skill retrieval, tool calls, environment responses, verifier states, branches, and fallbacks, and correlated events need not have causal effects. We introduce Causal Differential Skill Circuit Discovery (CDSCD), a framework for localizing and repairing counterfactual action disparities in fully recorded, replayable agent environments. Given matched tasks that differ only in a preregistered group signal, CDSCD uses paired randomized executions to construct typed event graphs and screens differential edges to reduce intervention cost. It evaluates ordered candidate paths using replay interventions that replace path events with matched-reference, group-neutral, or functionally equivalent states. A utility-constrained sparse objective selects candidate path sets on a discovery split. Independent confirmation tests then assess necessity, restoration, minimality, and outside-path exclusion. For validated circuits, CDSCD applies group-blind conditional rerouting that preserves non-sensitive execution context and uses functionally equivalent routes across groups. We evaluate the framework on synthetic graphs with known pathways, controlled natural-language tasks, and replayable multi-step environments, comparing it with attribution, predictive, and pruning baselines. Stability tests and explicit negative controls allow CDSCD to abstain when the evidence does not support a compact causal pathway. The resulting conclusions are restricted to manipulable mechanisms within the audited system and task distribution.